Community Structure In Networks Research Articles

Differences in Juniperus przewalskii Rhizosphere Microbiomes across Age Classes: Community Diversity and Assembly.

Evidence shows that biotic and abiotic factors have apparent diversity at different forest ages, leading to changes in rhizosphere microbiomes. However, the difference in diversity, co-occurrence pattern, and assembly of the rhizosphere microbial community among the different forest ages is still unclear. A total of 24 Juniperus przewalskii rhizosphere soil samples were selected from four representative age classes, using diameter at breast height (DBH) as a proxy for tree age (age class I: 5 < DBH ≤ 12.5 cm, age class II: 12.5 < DBH ≤ 22.5 cm, age class III: 22.5 < DBH ≤ 32.5 cm, and age class IV: DBH > 32.5 cm), and analyzed the structural characteristics of the soil microbial community by high-throughput amplicon sequencing. With the increase in age class, the microbial community α-diversity and β-diversity had an increased trend. The bacterial Shannon index in class II and class III were markedly higher than in class I. From class I to class IV, the relative abundances of dominant phyla such as Actinobacteria and Ascomycota decreased, and the relative abundances of Proteobacteria and Basidiomycota increased in contrast. The complexity and association stability of the bacteria and fungi community network structure increase with forest age. Stochastic processes mediated the assembly of soil bacterial communities, while deterministic processes played a more significant role in the assembly of fungal communities. In addition, the relative importance of deterministic components in the microbial community increased significantly with age class. Random forests suggested that soil pH, plant Shannon-Wiener index (H), and Pielou's evenness index (J) were the most important driving factors of bacterial and fungal community assembly. Overall, these results provide information useful for understanding the generation and maintenance mechanisms of rhizosphere microbial communities across age classes.

An innovative risk evaluation method on soil pathogens in urban-rural ecosystem

The presence and reproduction of pathogens in soil environment have significant negative impacts on soil security and human health in urban-rural ecosystem. Rapid urbanization has dramatically changed the land use, soil ecosystems, and the presence of pathogens in soil environment, however, the risk associated with soil pathogens remains unknown. Identifying the potential risk of pathogens in soils in urban-rural ecosystem has become an urgent issue. In this study, we established a risk evaluation method for soil pathogens based on analytic hierarchy process and entropy methods to quantitatively estimate the potential risk of soil pathogens to children and adults in urban-rural ecosystem. The abundance and species number of soil pathogens, network structure of soil microbial community, and human exposure factors were considered with 12 indicators to establish the risk evaluation system. The results revealed that 19 potential pathogenic bacteria were detected in soils within a typical urban-rural ecosystem. Substantial differences were observed in both abundance and species of soil pathogens as well as network structure of soil microbial community from urban to rural areas. Urban areas exhibited relatively lower levels of soil pathogenic abundance, but the microbial network was considerably unstable. Rural areas supported relatively higher levels of soil pathogenic abundance and stable microbial networks. Notably, peri-urban areas showed relatively unstable microbial networks alongside higher levels of soil pathogenic abundance compared to other areas. The risk evaluation of soil pathogens for both adults and children showed that peri-urban areas presented the highest potential risk, with children being more susceptible than adults to threats posed by soil pathogens in both urban and peri-urban areas. The established evaluation system provides an innovative approach for quantifying risk of soil pathogens at regional scale and can be used as a reference for preventing soil pathogens contamination and enhancing soil health in areas with intense human activities.

Selecting a significance level in sequential testing procedures for community detection

While there have been numerous sequential algorithms developed to estimate community structure in networks, there is little available guidance and study of what significance level or stopping parameter to use in these sequential testing procedures. Most algorithms rely on prespecifiying the number of communities or use an arbitrary stopping rule. We provide a principled approach to selecting a nominal significance level for sequential community detection procedures by controlling the tolerance ratio, defined as the ratio of underfitting and overfitting probability of estimating the number of clusters in fitting a network. We introduce an algorithm for specifying this significance level from a user-specified tolerance ratio, and demonstrate its utility with a sequential modularity maximization approach in a stochastic block model framework. We evaluate the performance of the proposed algorithm through extensive simulations and demonstrate its utility in controlling the tolerance ratio in single-cell RNA sequencing clustering by cell type and by clustering a congressional voting network.

A Community-Aware Framework for Social Influence Maximization

We consider the problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Influence Maximization</i> (IM), the task of selecting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Our experiments on real-world social networks show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence. We also study the effect of the community structure on the performance of the proposed framework. Our experiments show that the community structures with higher modularity lead the proposed framework to perform better in terms of run-time and influence.

User depression and severity level prediction during COVID-19 epidemic from social network data

In recent times, depression becomes a major issue, which causes suicide, particularly among youngsters. During the Coronavirus disease (COVID-19) epidemic, many organizations suggested social distance and quarantine actions, which cause major attention to the mental health and depression of each individual. Most population express their emotions by using modern social media technologies like Twitter, Facebook, etc. By considering user tweets, a Long Short-Term Memory (LSTM)-based classifier was designed that learns rich attributes such as psychological, contextual, cognitive, person-level and distress-dependent n-gram attributes of each user for depression severity level prediction. But, it did not learn the social network structural properties of the most prominent communities, which influences the prediction outcomes. Hence in this article, a novel model is developed to predict the user’s depression severity levels by considering the social network structure of most prominent communities and influence measures. At first, it analyses the physical characteristics of the most prominent groups based on their balanced local and global power distribution. Then, the influential users and communities are identified along with the rich group of attributes. Moreover, those attributes are provided to the LSTM classifier for the user’s depression severity level prediction during the COVID-19 epidemic. Finally, the investigational outcomes exhibit that the presented model attains 93.53% accuracy and 0.4376 Root Mean Square Error (RMSE) contrasted with the conventional classifiers to estimate the user’s depression severity level.

Limiting spectral distribution of stochastic block model

The stochastic block model (SBM) is an extension of the Erdős–Rényi graph and has applications in numerous fields, such as data analysis, recovering community structure in graph data and social networks. In this paper, we consider the normal central SBM adjacency matrix with [Formula: see text] communities of arbitrary sizes. We derive an explicit formula for the limiting empirical spectral density function when the size of the matrix tends to infinity. We also obtain an upper bound for the operator norm of such random matrices by means of the Stieltjes transform and random matrix theory.

Mapping the semi-nested community structure of 3D chromosome contact networks.

Mammalian DNA folds into 3D structures that facilitate and regulate genetic processes such as transcription, DNA repair, and epigenetics. Several insights derive from chromosome capture methods, such as Hi-C, which allow researchers to construct contact maps depicting 3D interactions among all DNA segment pairs. These maps show a complex cross-scale organization spanning megabase-pair compartments to short-ranged DNA loops. To better understand the organizing principles, several groups analyzed Hi-C data assuming a Russian-doll-like nested hierarchy where DNA regions of similar sizes merge into larger and larger structures. Apart from being a simple and appealing description, this model explains, e.g., the omnipresent chequerboard pattern seen in Hi-C maps, known as A/B compartments, and foreshadows the co-localization of some functionally similar DNA regions. However, while successful, this model is incompatible with the two competing mechanisms that seem to shape a significant part of the chromosomes' 3D organization: loop extrusion and phase separation. This paper aims to map out the chromosome's actual folding hierarchy from empirical data. To this end, we take advantage of Hi-C experiments and treat the measured DNA-DNA interactions as a weighted network. From such a network, we extract 3D communities using the generalized Louvain algorithm. This algorithm has a resolution parameter that allows us to scan seamlessly through the community size spectrum, from A/B compartments to topologically associated domains (TADs). By constructing a hierarchical tree connecting these communities, we find that chromosomes are more complex than a perfect hierarchy. Analyzing how communities nest relative to a simple folding model, we found that chromosomes exhibit a significant portion of nested and non-nested community pairs alongside considerable randomness. In addition, by examining nesting and chromatin types, we discovered that nested parts are often associated with active chromatin. These results highlight that cross-scale relationships will be essential components in models aiming to reach a deep understanding of the causal mechanisms of chromosome folding.

Hybridized Hierarchical Watermarking and Selective Encryption for Social Image Security.

With the advent of cloud computing and social multimedia communication, more and more social images are being collected on social media platforms, such as Facebook, TikTok, Flirk, and YouTube. The amount of social images produced and disseminated is rapidly increasing. Meanwhile, cloud computing-assisted social media platforms have made social image dissemination more and more efficient. There exists an unstoppable trend of fake/unauthorized social image dissemination. The growth of social image sharing underscores potential security risks for illegal use, such as image forgery, malicious copying, piracy exposure, plagiarism, and misappropriation. Therefore, secure social image dissemination has become urgent and critical on social media platforms. The authors propose a secure scheme for social image dissemination on social media platforms. The main objective is to make a map between the tree structure Haar (TSH) transform and the hierarchical community structure of a social network. First, perform the TSH transform on a social image using social network analysis (SNA). Second, all users in a social media platform are coded using SNA. Third, watermarking and encryption are performed in a compressed domain for protecting social image dissemination. Finally, the encrypted and watermarked contents are delivered to users via a hybrid multicast-unicast scheme. The use of encryption along with watermarking can provide double protection for social image dissemination. The theory analysis and experimental results demonstrate the effectiveness of the proposed scheme.

Large positive ecological changes of small urban greening actions

Abstract The detrimental effects of environmental change on human and non‐human diversity are acutely manifested in urban environments. While urban greenspaces are known to mitigate these effects and support functionally diverse ecological communities, evidence of the ecological outcomes of urban greening remains scarce. We use a longitudinal observational design to provide empirical evidence of positive ecological changes brought about by greening actions. We collected a plant–insect interactions data set 1 year before, and for 3 years after, a greenspace received a small greening action within a densely urbanised municipality. We then assessed how (i) insect species richness; (ii) the probabilities of occurrence, survival and colonisation of the insect community; and (iii) the plant–insect network structure varied across the 4 years of the study. As we understand, this is the first study to apply statistical and network analytical frameworks to quantitatively track how positive ecological changes accrue over time at a site after the implementation of a specific urban greening action. We show how a small greening action quickly led to large positive changes in the richness, demographic dynamics and network structure of a depauperate insect community. An increase in the diversity and complexity of the plant community led to, after only 3 years, a large increase in insect species richness, a greater probability of occurrence of insects within the greenspace and a higher number and diversity of interactions between insects and plant species. We demonstrate how large positive ecological changes may be derived from investing in small greening actions and how these contribute to bring indigenous species back to greenspaces where they have become rare or been extirpated by urbanisation. Our findings provide crucial evidence that supports best practice in greenspace design and contributes to re‐invigorate policies aimed at mitigating the negative impacts of urbanisation on people and other species.

Study on characteristics and community structure of high-speed rail network centered on Shanghai

The high-speed railway is a typical representative of China 's high-speed transportation infrastructure, with typical complex network characteristics. Based on the complex network theory, this paper conducts an empirical study on a Space-P network model for high-speed train originating from Shanghai. Then the network is divided into communities by louvain algorithm. The average distance of the network is 1.76, the modularity of community division is 0.3683, and the clustering coefficient is 0.6859, which is typical of small-world network. Due to Space-P model, the network is highly connected, resulting in a relatively low modularity. To a certain extent, the network reflects the developed high-speed rail level and wide direct range of Shanghai, and also reflects the convenience of China 's high-speed rail.

Convergence of soil bacterial communities with time and reduction of bacterial interaction complexity in response to meso- and microplastic stress

The ecological consequences of meso- and microplastics (MMPs) on soil health are far from being understood, especially lacking information on how MMPs perturbations affect soil bacterial succession patterns. This work investigated how mulching film-derived MMPs existence affected soil properties and bacterial characteristics, and if such changes were reflected in the architecture of biotic networks. The obtained results suggested that MMPs exerted potential changes in soil nutrients and enzymatic activities, and the extent of such response was strongly related to MMPs characteristics and exposure time. The abundance of the top ten phyla in MMPs-treated soil significantly varied with the incubation time before Day 45, but gradually converged over the following 45 days. Besides, the existence of MMPs increased biotic network complexity in the short run but reduced the complexity with the prolonging of time. MMPs significantly altered the network structure of soil microbial communities at the keystone species level, and the number of the keystone taxa shaping network showed a descent tendency over time. According to Partial Least Square Structural Equation Model (PLS-SEM) analysis, MMPs pollution could significantly and directly affect soil nutrients, further changing soil microbial characteristics. Additionally, soil microbes were also indirectly regulated by soil enzymatic activities in MMPs-polluted soil. This work could provide insights into the potential ecological effects of mulching film residues in soil environments.

A nature-inspired algorithm to find community structure in complex networks

Complex networks have in generally communities. These communities are very important. Network’s communities represent sets of nodes, which are very connected. In this research, we developed a new method to find the community structure in networks. Our method is based on flower pollination algorithm (FPA) witch is used in the splitting process. The splitting of networks in our method maximizes a function of quality called modularity. We provide a general framework for implementing our new method to find community structure in networks. We present the effectiveness of our method by comparison with some known methods on computer-generated and real-world networks.

The role of microbiota during chicken manure and pig manure co-composting

Co-composting is an excellent and effective technology for treating livestock manure in which microorganisms play a crucial function. Therefore, this study aimed at investigating the changes of microbial interactions during co-composting. Six different addition ratios of chicken and pig manure were used in composting experiment. The results showed that the co-composting system using 60% chicken manure and 40% pig manure significantly altered the microbial diversity and community structure. In addition, the complexity and tightness of its microbial community network structure reached the maximum, as did the strength of its cooperative and competitive microbial interactions. The higher microbial abundance and microbial interaction have the potential to promote the decomposition and transformation of compost components. Therefore, this study preliminarily revealed the changes of microbial community in co-composting, which provided a theoretical basis for optimizing microbial community interaction in composting systems by mixing different ratios of materials in practice.

Threshold Selection for Brain Connectomes.

Introduction: Structural and functional brain connectomes represent macroscale data collected through techniques such as magnetic resonance imaging (MRI). Connectomes may contain noise that contributes to false-positive edges, thereby obscuring structure-function relationships and data interpretation. Thresholding procedures can be applied to reduce network density by removing low-signal edges, but there is limited consensus on appropriate selection of thresholds. This article compares existing thresholding methods and introduces a novel alternative "objective function" thresholding method. Methods: The performance of thresholding approaches, based on percolation and objective functions, is assessed by (1) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and by (2) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. An application to empirical data is provided. Results: Our proposed objective function-based threshold exhibits the best performance in terms of resulting in high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks. Discussion: Existing network thresholding methods yield widely different results when graph metrics are subsequently computed. Thresholding based on the objective function maintains a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides a proof of principle that objective function thresholding could offer a useful approach to reducing the network density of functional connectivity data.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015–2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

Hierarchical community structure in networks.

Modular and hierarchical community structures are pervasive in real-world complex systems. A great deal of effort has gone into trying to detect and study these structures. Important theoretical advances in the detection of modular have included identifying fundamental limits of detectability by formally defining community structure using probabilistic generative models. Detecting hierarchical community structure introduces additional challenges alongside those inherited from community detection. Here we present a theoretical study on hierarchical community structure in networks, which has thus far not received the same rigorous attention. We address the following questions. (1) How should we define a hierarchy of communities? (2) How do we determine if there is sufficient evidence of a hierarchical structure in a network? (3) How can we detect hierarchical structure efficiently? We approach these questions by introducing a definition of hierarchy based on the concept of stochastic externally equitable partitions and their relation to probabilistic models, such as the popular stochastic block model. We enumerate the challenges involved in detecting hierarchies and, by studying the spectral properties of hierarchical structure, present an efficient and principled method for detecting them.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal “attacks” (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

Finding and Evaluating Community Structures in Spatial Networks

Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not been tested sufficiently so far. This study first introduced a spatial autoregressive and gravity model united method (SARGM) to simulate benchmark spatial networks with known regional distributions. Then, a novel spectral clustering-based spatial community detection method (SCSCD) was proposed to identify spatial communities from eight kinds of benchmark spatial networks. Comparative experiments on SCSCD and three other methods showed that SCSCD performed the best in accuracy and effectiveness. Moreover, the scale parameter and the community number setting of the SCSCD were investigated experimentally. Finally, a case study was applied to the SCSCD to demonstrate its ability to extract the internal community structure of a high-speed train network in China.

Higher Order Connection Enhanced Community Detection in Adversarial Multiview Networks.

Community detection in multiview networks has drawn an increasing amount of attention in recent years. Many approaches have been developed from different perspectives. Despite the success, the problem of community detection in adversarial multiview networks remains largely unsolved. An adversarial multiview network is a multiview network that suffers an adversarial attack on community detection in which the attackers may deliberately remove some critical edges so as to hide the underlying community structure, leading to the performance degeneration of the existing approaches. To address this problem, we propose a novel approach, called higher order connection enhanced multiview modularity (HCEMM). The main idea lies in enhancing the intracommunity connection of each view by means of utilizing the higher order connection structure. The first step is to discover the view-specific higher order Microcommunities (VHM-communities) from the higher order connection structure. Then, for each view of the original multiview network, additional edges are added to make the nodes in each of its VHM-communities fully connected like a clique, by which the intracommunity connection of the multiview network can be enhanced. Therefore, the proposed approach is able to discover the underlying community structure in a multiview network while recovering the missing edges. Extensive experiments conducted on 16 real-world datasets confirm the effectiveness of the proposed approach.

Leveraging the Community Structure of a Social Network for Maximizing the Spread of Influence

We consider the problem of Influence Maximization (IM), the task of selecting k seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on a real-world social network show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence.